Extreme Event Analysis in Next Generation Simulation Architectures

被引：3

作者：

Hamilton, Stephen ^{[1
]}

Burns, Randal ^{[1
]}

Meneveau, Charles ^{[2
]}

Johnson, Perry ^{[2
]}

Lindstrom, Peter ^{[3
]}

Patchett, John ^{[4
]}

Szalay, Alexander S. ^{[5
]}

机构：

[1] Johns Hopkins Univ, Dept Comp Sci, Baltimore, MD 21218 USA

[2] Johns Hopkins Univ, Dept Mech Engn, Baltimore, MD 21218 USA

[3] Lawrence Livermore Natl Lab, Livermore, CA 94550 USA

[4] Los Alamos Natl Lab, Los Alamos, NM 87545 USA

[5] Johns Hopkins Univ, Dept Phys & Astron, Baltimore, MD 21218 USA

来源：

HIGH PERFORMANCE COMPUTING (ISC HIGH PERFORMANCE 2017) | 2017年 / 10266卷

基金：

美国国家科学基金会;

关键词：

IDENTIFICATION;

D O I：

10.1007/978-3-319-58667-0_15

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Numerical simulations present challenges because they generate petabyte-scale data that must be extracted and reduced during the simulation. We demonstrate a seamless integration of feature extraction for a simulation of turbulent fluid dynamics. The simulation produces on the order of 6 TB per timestep. In order to analyze and store this data, we extract velocity data from a dilated volume of the strong vortical regions and also store a lossy compressed representation of the data. Both reduce data by one or more orders of magnitude. We extract data from user checkpoints in transit while they reside on temporary burst buffer SSD stores. In this way, analysis and compression algorithms are designed to meet specific time constraints so they do not interfere with simulation computations. Our results demonstrate that we can perform feature extraction on a world-class direct numerical simulation of turbulence while it is running and gather meaningful scientific data for archival and post analysis.

引用

页码：277 / 293

页数：17

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